Sustainability Assessment of the Agricultural and Energy Systems of Senegal
Bibliographic record
Abstract
To improve decision-making, sustainability-based approaches to assessment of options and undertakings demand that we move beyond narrowly defined considerations to address the full suite of requirements for progress towards sustainability. This paper reports on a sustainability assessment exercise that originally focused on burning agricultural residues, primarily peanut shells, for cooking applications in Senegal. The scope of assessment had to be expanded to address the agricultural and energy systems of Senegal, when closer examination revealed a complex set of energy and agricultural system interactions that could undermine the anticipated positive effects of initiatives centred primarily on peanut residue cookstoves. The case highlights the need to be open to expanding the scope of assessment to address underlying and/or unexpected issues that cannot be addressed appropriately at the project scale. In particular, the case illustrates how the assessment of an energy system may serve as an entry point into a deeper exploration of the context in which the energy system is embedded. The analysis also illustrates a situation in which different paths that may be followed, each with its own degree of uncertainty, path dependence, feasibility, fairness, cultural sensitivity, trade-off acceptability and possibilities for public judgement of overall desirability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".